Once the computation offloading and resource allocation decisions have been made, cooperation among multiple edge servers is important to further balance the workload and improve computation energy efficiency. In a dynamic traffic environment, transferring workload between edge/cloud servers will consume more communication energy, and the intermittent connections between vehicles and edge/cloud servers may decrease the QoS and lead to extra energy cost in rebuilding network connections. In [1]}, the authors propose a heuristic algorithm to balance the computation workload among multiple RSUs and minimize the energy consumption due to processing the offloaded tasks under latency constraints. In the considered system, multiple RSUs equipped with MEC servers are connected via fault-free and (almost) delay-free wired links. They periodically broadcast the current network status (i.e., traffic loads and computation capabilities) to other RSUs. When a vehicle uploads the computation tasks to the nearest RSU, it evaluates the service time of these computation tasks based on the incoming traffic flows. If the expected service time is longer than the tolerable delay, the computation task will be transferred to other RSUs via multihop transmission. In the cooperative computation offloading process, the total energy consumption includes the energy consumption in task processing, task transmission among RSUs, and returning the computation results. To minimize the energy consumption of all the computation tasks while satisfying the delay constraint, a MEC-enabled Energy-Efficient Scheduling (MEES) scheme is proposed to schedule computation tasks among MEC servers and feed back the results in an energy-efficient manner to reduce the downlink transmission energy consumption of RSUs.
